For face recognition, Gabor features are effectively used. But, only a few approaches used Gabor phase features and they are performing worse than the Gabor magnitude features. To determine the potential of Gabor phase and its fusion with magnitude for face recognition, in this paper, we have proposed local Gabor XOR pattern (LGXP) operator, which encode Gabor phase. Then we introduce block-based Fisher’s linear discriminant (BFLD) for reduce dimensionality of proposed operator and at same time discriminative power also get enhanced. At last, by using BFLD we fuse Gabor phase and Gabor magnitude for face recognition. We evaluate our method for FERET database. Also, we perform comparative experimental studies of different local patterns.DOI:http://dx.doi.org/10.11591/ijece.v2i3.279

Gonde, Anil Balaji

Randive, Santosh Nagnath

English

Institute of Advanced Engineering and Science

International Journal of Electrical and Computer Engineering (IJECE) Vol.2, No.3, June 2012, pp. 345~352 ISSN: 2088-8708      345   Journal homepage: http://iaesjournal.com/online/index.php/IJECE Face Detection Face Representation Face Classification A Novel Approach for Face Recognition Using Fusion of Local Gabor Patterns   Santosh Randive, Anil Gonde Electronics and Telecommunication Department  SGGSIE & T, Nanded, India   Article Info  ABSTRACT  Article History: Received Feb 16th, 2012 Revised Mar 25th, 2012 Accepted Apr 11th, 2012   For face recognition, Gabor features are effectively used. But, only a few approaches used Gabor phase features and they are performing worse than the Gabor magnitude features. To determine the potential of Gabor phase and its fusion with magnitude for face recognition, in this paper, we have proposed local Gabor XOR pattern (LGXP) operator, which encode Gabor phase. Then we introduce block-based Fisher’s linear discriminant (BFLD) for reduce dimensionality of proposed operator and at same time discriminative power also get enhanced. At last, by using BFLD we fuse Gabor phase and Gabor magnitude for face recognition. We evaluate our method for FERET database. Also, we perform comparative experimental studies of different local patterns.  Keyword: Face representation Fisher’s linear discriminant  Histogram,  Fusion,  Local gabor XOR pattern   Copyright © 2012 Institute of Advanced Engineering and Science.  All rights reserved. Corresponding Author: Santosh Randive,  Electronics and Telecommunication Department  SGGSIE & T, Nanded, India Email: santoshrandive89@gmail.com   1. INTRODUCTION   Face recognition is one of the most typical applications in image processing. Its used in many areas such as entertainment, information security and surveillance [11]. In last few decades, numerous approaches have been proposed for face recognition and progress is made. But still many challenges remains in it due to very small person to person variations and variations arising due to illumination, pose, expression and many other factors.                                                                          Input face image                                                                                            Normalized image                                                                                              Image features                              Figure 1. Typcal face recognition system.                  ISSN: 2088-8708  IJECE  Vol. 2, No. 3, June 2012 :  345 – 352 346Basically, in face recognition, therearethree main stages: facedetection, face representation and face classification. The most important stage is face representation shown in Fig. 1 The key for the successful face recognition is to get internal representation from the normalized face images. Image representation consists of two procedures face design and feature extraction. Face design generate face descriptor from one face image using some signal processing technique (e.g., Gabor wavelet [4], Local binary pattern (LBP) [1]). And feature extraction generates low-dimensional features by applying subspace analysis (e.g., Principal component analysis (PCA) [7], linear discriminant analysis (LDA) [2]. In earlier years, geometrical features (e.g., nose width and length, mouth position and chin shape) and image template were widely used for feature design. But they are easily affected by variation in facial appearance. Later on, feature design done in frequency domain or wavelet domain attracted much attention for face recognition, such as discrete cosine transform (DCT) [12], discrete wavelet transform (DWT)[3], and Gabor wavelet [4], [5]. But the face representation using Gabor wavelet is well known and more successful. Recently, few good methods like LBP, local Gabor binary pattern (LGBP) [9], and histogram of Gabor phase pattern (HGPP) [8] are proposed which are called local Gabor pattern in this paper. In this paper a novel approach for face recognition using 2-D Gabor wavelet, LBP and LXP for feature extraction and BFLD for feature dimension reductionis proposed. The rest of the paper is organized as follows: Section 2 gives the related work. Proposed method is explained in Section 3. Experimental results are reported in Section 4 and conclusion is given in Section 5.   2.    RELATED WORK In this section, we firstly describe Gabor wavelet representation, LBP and LGXP respectively in 2-A,2-B and 2-C and comparison between local patterns in 2-D.  2.1.  Gabor Wavelet Representation It was originally introduced by Dennis Gabor for 1-D signals and Daugman extended the Gabor filter for 2-D. The Gabor filters are band pass used for feature extraction. Gabor filters similar to STFT or windowed Fourier transform, have both frequency-selective and orientation-selective and have optimal joint resolution in spatial and frequency domain.  The 2-D Gabor filter is sinusoidal plane of particular frequency and orientation, modulated by a Gaussian envelope. Typical Gabor features, such as Gabor feature space consists of response calculated by Gabor filters at several different orientations and scales (frequencies): a filter bank. A Bank of filters is used with different orientations so as to extract frequency information and hence the features at different orientations,since all facial features are not present at same orientation. Scaling is done at each orientation so as to get maximum frequency information at each orientation i.e., orientation and scaling helps in extracting maximum frequency information. A 2-D Gabor kernel is given by,  , =||,||(||,||||||/)[, − /]                 (1)  where  and  defines the orientation and scale of Gabor kernels,  = (, ), ||. || denotes the norm operator and wave vector 	, is defined as follows:   	, = 	 	                      (2)  where 	 = 	/and  = /8. 	is the maximum frequency and  is the spacing factor between the kernels in frequency domain.  A filter bank consisting of several filters need to be used because relationships between responses provide the basis for distinguishing objects. The selection of discrete rotation angles is such that the orientation must be spaced uniformly.   =       ,  = {0,1, . . ,  − 1}                    (3)  IJECE   A Novel Approach for Face Recognition using Fusion of Local Gabor Pattern (Santosh Randive)where  is  orientation and reduced to half since angle of response  [valued input.    Frequency selection is given by, 	  	      ,   0,1, . . ,  Useful values for  includes     The Gabor kernels in (1) all are selfwavelet, by scaling and rotation via wave vectorcomplex plane wave, while the first term in square brackets in (1) determines the sinand the second term  is used to get zero DC response i.e., make Gabor filter insensitive to background luminance level determines the ratio of Gaussian window width to wavelength.Gabor wavelets of five different scales are used for feature extraction. Therefore, a filter bank consists of 40 filterswith half octave spacing in radial direction and one octave spatial bandwidth, for   √2.  2.2. Local Binary Patterns (LBP)The LBP operator assigns a label to every pixel of an imageneighbourhood of each pixel with the center pixel value and considering the result as a binary number. For example, as shown in Fig. 2, “11010011” is the designed pattern of the center pixel. By applying the LBP     Figure 2.  Operator to one facial image, one pattern map can be computed. Then, the pattern map is dividinto many block and then histogramis computed in each block and concatenated together to form the description of the input facial image [1]. 2.3. Local Gabor XOR patterns (LGXP)The basic idea of this method is that,different ranges and then LXP operator is applied to the quantize phases of the central pixel and of each its    Figure 3. Example of the encoding Finally, the resulting binary labels are concatenated together as the local pattern of the central pixel. 2.4. Comparison with Other Local Gabor PatternThere are various ways to get local Gabor patterns.The real and imaginary part of Gabor followed by LXP operator gives Re LGPP and Im_LGPP [8] respectively. The magnitude part of Gabor followed by LBP give LGBP_Mag [9] and phase part of Gabor followed byboth LBP and LXP gives LGBP_Pha[10] and LGXP respectively. ISSN: 2088-8708  is the total number of orientations to be used. The computations can be  2] are complex conjugates on responses on [  1      2 for octave spacing and   √2 for half octave spacing.-similar since they can be generated from one kernel, the mother  	,. Each kernel is a product of a Gaussian envelope and usoidal part of the kernel    0,1,2,3,4, and eight different orientations    by thresholding the 3 LBP operator define din3 neighbourhood.    as shown In Fig. 3, the phases are   method of LGXP where the phase is quantized   3470  ] in case of real              (4)    0,1,2,3,4,5,6,7  2, 	  , and ×3 .  ed firstly quantized into   into 4 ranges           IJECE  Vol. 2, No. 3, June 2012348  3. PROPOSED METHOD Face recognition system is shown inclassification. In face detection, all the face images are aligned based on the manually located eye centers provided by the original database, and then normalized to 80and then local binary patterns(i.e., LGBP and LGXP) are used.                                          Figure   For a feature extraction, Gabor kernel convolved with the input image. Η   , ∗ "   where # is the convolution result, operator and   , .            Convultion *          Figure  Then LBP and LXP operator operates on different Gabor parts (i.e., real, imaginary, magnitude and phase) which gives LGBP and LGXP shown in Fig.5.The proposed LGXP descriptor can be directly applied to face recognition system bysimilarity measurement. However, this is not good enough since the feature dimension is very high due to multiple Gabor filter (e.g., 40 in this study). In theory, to reduce dimensionality, we can apply FLD directly; but, in case of so high(SSS)” problem. So, further we present blockThe basic idea of BFLD is firstly to divide the highInput Image 40 Gabor Filters Input  Face image detectionRecognized face image  :  345 – 352  Fig.4 consist of face detection, feature extraction and face ×88 pixels. For feature extraction Gabor filters                                4. Blocked diagram of face recognition system         , is the Gabor kernel, " is input image,  5. Steps involved in feature extraction   -dimensional feature, FLD suffers from heavy “small sample size -based FLD (BFLD) approach. -dimensional LGXP descriptor into multiple Normalized Face Image (80x88) LGBP & LGXP LBP & LXP operator Gabor Feature    Face  Feature extraction Dimension ReductionFusion ClasificationRecognition Face          ISSN: 2088-8708              (5) ∗ is convolution  using $. .,  % 40 % 2   IJECE  ISSN: 2088-8708   A Novel Approach for Face Recognition using Fusion of Local Gabor Pattern (Santosh Randive) 349feature segments (corresponding to different spatial blocks in the face image), then apply FLD to each segment, and finally combine the decisions of all the block-wise FLD. By such a “divide and conquer” strategy,the SSS problem is greatly weakened since the dimensionality of the input feature for each FLD is much lower.   Fig.6. Flow chart of the BFLD feature extraction approach  Figure 5 illustrates the flow chart of the BFLD approach. Briefly speaking, for each face image, we firstly calculate its multiple LGXP maps. Then, we divide these pattern maps into multiple non-overlapping blocks and then we take histogram of each block and concatenate and calculate the block based representations. Based on the training set, we learn the FLD matrics to calculate the low-dimensional features for each block.    Figure 7. Fusion of LGBP_Mag and LGXP. (a) Feature-levelfusion.(b)Score-levelfusion.  The block partition strategy shown in Fig. 7(a) for LGXP and in Fig. 7(b) for BFLD approach. In LGXP the pattern map is directly divided into  sub-blocks but; while in BFLD pattern map divided in  & blocks and each block partitioned into  ' sub-blocks and  better representation is obtained  using only one histogram. At last, fusion of LGBP_Mag and LGXP required. The two fusion approaches are described in detailsas follows. 1. Feature-level fusion. As shown in Fig. 8(a), for the $ block, we represent it as one vector by concatenating its LGBP_Mag histograms and LGXP histograms. Then by using BFLD approach calculate low-dimensional feature vector Fi. For the Gallery block "and its corresponding probe block ", the similarity between them is calculated as follows:             ()", "* = +$),, ,* =.||||.|||| (6)  2. Score-level Fusion. As shown in Fig. 8(b), from the two sequences of $ block, #_, and #,, we respectively extract their low-dimensional FLD features, ,_, and ,,.Then we respectively computes two similarities between the Gallery block " and its corresponding probe block " namely (, and (,.Finally, these two similarities are fuse together according to weighted sum rule,  ()", "* = -. (, + 1 − -. (,  where w denote the weight of LGBP_Mag and varies within [0,1]          IJECE  Vol. 2, No. 3, June 2012350. Figure 8. Fusion of LGBP_Mag  The similarity between thr Gallery and Probe face corresponding block according to the sum rule [13].  (", "  ∑ (", "    In face classification, for recognize the input unknown face image is usually classified as the face which is most similar to input one, if the maximal similarity occurs face   4. EXPERIMENTAL RESULTSIn this paper the FERET database is used. For details refer [6].standard gallery (1,195 images of 1,195 subjects), we test the recognition rate of our approaches on the four probe sets: Fb (1,195 images of 1,195 subjects), Fc (194 images of 194 subjects), Duplicate I (abbreviated as “DupI”,722 images of 243 subjects) and Duplicate II (abbreviated as “DupII”,722 images of 243 subjects). All face images are normalized to 80  Table  MethodsLGBP_MagLGBP_PhaRe_LGPPIm_LGPPLGXP   Table MethodsLGBP_Mag+BFLDLGBP_Pha+BFLDRe_LGPP+BFLDIm_LGPP+BFLDLGXP+BLFD          :  345 – 352  and LGXP. (a) Feature-levelfusion. (b) Scoreimagesis calculated by fusing the similarities of       is recognized.  In our experiments, based on %88 pixels and the size of Gabor filter window is set to 321. Recognition Rate with Local Gabor Pattern  FERET Probe Sets Fb Fc DupI DupII     96% 96% 74% 70%     97% 97% 80% 79%     98% 99% 80% 78%     98% 99% 76% 73%    98% 100% 82% 83%  2. Recognition Rate Combination With BFLD  FERET Probe Sets Fb Fc Dup I Dup 99% 99% 91% 91% 99% 99% 88% 86% 99% 100% 91% 87% 99% 100% 90% 88% 99% 100% 92% 91%       ISSN: 2088-8708  -levelfusion.               (7)  %32 pixels.  II      IJECE  ISSN: 2088-8708   A Novel Approach for Face Recognition using Fusion of Local Gabor Pattern (Santosh Randive) 351 Table 3. Recognition Rate With Fusing Different Gabor Parts  Methods FERET Probe Sets Fb Fc Dup I Dup II S[LGBP_Mag+LGBP_Pha] 99% 99% 92% 92% S[Re_LGPP+Im_LGPP] 99% 100% 92% 89% S[LGBP_Mag+Re_LGP]P 99% 99% 93% 91% S[LGBP_Mag+LGXP] 99% 99% 93% 92% S[LGBP_Mag+LGXP] 99% 99% 94% 93%   The results of all experiments are shown in Table 1, 2 and 3. First, the proposed LGXP method performs best in case of Fb and Fc but not for DupI and Dup II. The interesting improvement can be achieved in second method i.e., BFLD with local Gabor patterns especially in case of Dup I and Dup II. The third method fusion of LGBP_Mag and LGXP leads to best improvement and specially for ([/012 + /032].   5. CONCLUSION In this paper, we have mainly work on Gabor phase information, as well as its fusion with Gabor magnitude. By using local patterns we encode the Gabor phase called LGXP. Then we introduce BFLD and studied the fusion methods of different local patterns of Gabor features. These methods achieve better or comparable results than the best known ones.   ACKNOWLEDGMENT  The authors would like to thank Prof. S. N. Talbar for their cooperation and National Institute Of Standard and Technology (NIST) for providing FERET database.   REFERENCES  [1]  T. Ahonen, A. Hadid, and M. Pietikäinen, “Face Description with Local Binary Patterns: Application to Face Recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no.12, pp. 2037–2041, Dec. 2006. [2]  P.N. Belhumeur, J.P. Hespanha, and D.J. Kriegman, “Eigenfacesvs. Fisherfaces: Recognition using Class Specific Linear Projection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 7, pp. 711–720, Jul. 1997. [3]  J.T. Chienand, C.C. Wu, “Discriminant Wavelet Faces and Nearest Feature Classifiers for Face Recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 12, pp. 1644–1649, Dec. 2002. [4]  J.Daugman, “Un Certainty Relation for Resolution in Space, Spatial Frequency, and Orientation Optimized by Two-Dimensional Visual Cortical Filters,”  J. Opt. Soc. Amer. A, vol. 2, no. 7, pp. 1160–1169, 1985. [5]  J.P. Jonesand, L. Palmer, “Anevaluation of the Two Dimensional Gabor Filter Model of Simple Receptive Fields in Catstriate Cortex,” J. Neurophysiol., vol. 58, no. 6, pp. 1233–1258, 1987. [6]  P.J. Phillips, H. Moon, S.A. Rizvi, and P.J. Rauss, “The Feret Evaluation Methodology for Face-Recognition Algorithms,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no.10, pp. 1090–1104, Oct. 2000. [7]  M. Turkand A. Pentland, “Eigen Faces for Recognition,” J.Cogn. Neu-Rosci., vol. 3, no.1, pp. 71–86, 1991. [8]  B. Zhang, S.Shan, X. Chen, and W. Gao, “Histogram of Gabor Phase Patterns (HGPP): A Novel Object Representation Approach for Face Recognition,” IEEE Trans. Image Process., vol. 16, no.1, pp. 57–68, Jan. 2007. [9]  W. Zhang, S. Shan, W. Gao, X. Chen, and H. Zhang, “Local Gabor Binary Pattern Histogram Sequence (LGBPHS): A Novel non-Statistical Model for Face Representation and Recognition,” in Proc. Int. Conf. Computer Vision, 2005, pp. 786–791. [10]  W. Zhang, S. Shan, X. Chen, and W. Gao, “Are Gabor Phases Really Useless for Face Recognition?” in Proc. Int. Conf. Pattern Recognition, 2006, pp. 606–609. [11]  W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, “Face Recognition: A Literature Survey,” ACM Comput. Surv., vol. 35, no.4, pp. 399–458, 2003. [12]  Z. M. Hafedand M. D. Levine, “Face Recognition using the Discrete Cosinetrans Form,” Int. J. Comput. Vis., vol. 43, no. 3, pp. 167–188, 2001. [13]  J. Kittler, M. Hatef, R.P.W. Duin, and J. Matas, “On Combining Classifiers,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 3, pp. 226–239, Mar. 1998.                     ISSN: 2088-8708  IJECE  Vol. 2, No. 3, June 2012 :  345 – 352 352      

A Novel Approach For Face Recognition Using Fusion Of Local Gabor Patterns

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A Novel Approach For Face Recognition Using Fusion Of Local Gabor Patterns

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